The survey was conducted by telephone by the national survey research
organization of Schulman, Ronca & Bucuvalas, Inc (SRBI). A national household sample
was constructed using random digit dialing. Each household was screened to determine the
number of adult (age 16 or older) drivers in the household. One eligible driver was
systematically selected in each eligible household by the interviewers, using
computer-assisted telephone interviewing (CATI) to reduce interview length and minimize
recording errors. A Spanish-language translation and bilingual interviewers were used to
minimize language barriers to participation. The survey was conducted between February 20
and April 11, 1997. The telephone interviews averaged 30 minutes in length. A total of
6,000 interviews were completed with a participation rate of 73.5 percent.

Since this was the first national survey of speeding and unsafe driving
practices the number of issues to be covered was extensive. In order to accommodate the
number of questions required without unduly burdening the public, two versions of the
questionnaire were developed. One questionnaire (Version 1) focused primarily on speeding
issues. The other questionnaire (Version 2) focused primarily on other forms of unsafe
driving. Each version was fielded as an independent national sample, constructed in an
identical fashion. Hence, for some questions we have national estimates based on sample
sizes of 3,000, while estimates for core questions about speeding and unsafe driving
behavior, as well as driver and driving characteristics shared by both versions, are based
on sample sizes of 6,000.

TABLE 1

Unweighted Size of Sample
Components

Split-Half

Total

A

B

Version 1 - Speeding

1,489

1,511

3,000

Version 2 - Unsafe Driving

1,467

1,533

3,000

Total

2,956

3,044

6,000

In addition to these component sample sizes, in a few instances a
specific question was asked of a cross sample. That is, Version 1, Split A together with
Version 2, Split B for an unweighted sample size of 3,022. The complement would be 2,978
which is made up of Version 1, Split B together with Version 2, Split A (see Table 1).

SAMPLE CONSTRUCTION

Most of the statistical formulas associated with sampling theories
are based upon the assumption of simple random sampling. Specifically, the statistical
formulas for specifying the sampling precision (estimates of sampling variance), given
particular sample sizes, are premised on simple random sampling. Unfortunately, random
sampling requires that all of the elements in the population have an equal chance of being
selected. Since no enumeration of the total population of the United States (or its
subdivisions) is available, all surveys of the general public are based upon an
approximation of the actual population and survey samples are generated by a process
closely resembling true random sampling.

The survey sample was based on a modified stratified random digit
dialing method (RDD), using an area probability/RDD sample rather than a single-stage/RDD
sample. There are several important advantages to using an area probability base: (1) it
draws the sample proportionate to the geographic distribution of the target population
rather than the geographic distribution of telephone households, which is vital to
constructing unbiased population estimates from telephone surveys; (2) it allows greater
geographic stratification of the sample to control for known geographic differences in
non-response rates; and (3) it facilitates the use of Census estimates of population
characteristics to weight the completed sample to correct for other forms of sampling
bias. Moreover, the precision of sample estimates is generally improved by stratification.

Hence, as specified for the study design for the survey, the adult
household population of the United States was stratified by the 10 NHTSA regions. The
estimated distribution of the population by stratum was calculated on the basis of the
Bureau of the Census, Resident Population of the United States, Regions and States by
Selected Age Groups and Sex: April 1, 1990 Census and July 1, 1990 to July 1, 1995
Estimates (release date, August 1996). At the time of the survey, these were the most
recent projections of the distribution of adult population by state. Based on these Census
data on the geographic distribution of the target population, the total sample was
proportionately allocated by stratum. The geographic allocation of the cross-sectional
sample for the survey is presented in Table 2 (next page).

Once the sample had been geographically stratified with sample
allocation proportionate to population distribution, a sample of assigned telephone banks
were randomly selected from an enumeration of the Working Residential Hundred Blocks of
the active telephone exchanges within the region. The Working Hundreds Blocks were defined
as each block of 100 potential telephone numbers within an exchange that included 3 or
more residential listings. (Exchanges with one or two listings were excluded because in
most cases such listings represent errors in the published listings).

TABLE 2

Population Aged 16 and Over by NHTSA Region

Region

States Covered

Population

Sample Size

Number

Percent

Total U.S.

266,043,000

100.0%

6,000

I

CT, ME, MA, NH, RI, VT

13,194,000

5.0

297

II

NJ, NY

26,113,000

9.8

589

III

DE, DC, MD, PA, VA, WV

27,140,000

10.2

612

IV

AL, FL, GA, KY, MS, NC, SC, TN

48,851,000

18.4

1,102

V

IL, IN, MI, MN, OH, WS

48,542,000

18.2

1,095

VI

AR, LA, NM, OK, TX

30,761,000

11.6

694

VII

IA, KS, MO, NE

12,478,000

4.7

281

VIII

CO, MT, ND, SD, UT, WY

8,521,000

3.2

192

IX

AZ, CA, HI, NV

39,799,000

15.0

898

X

AK, ID, OR, WA

10,644,000

4.0

240

Total does not add to 100% due to rounding.

Source: Population Projections for States, by Age, Sex, Race, and Hispanic Origin: 1993 to 2020
(Current Population Reports, P25-1111), U.S. Bureau of the Census

Total does not add to 100%
due to rounding.

Source: Population Projections for States, by Age, Sex, Race, and
Hispanic Origin: 1993 to 2020 (Current Population Reports, P25-1111), U.S. Bureau of
the Census

The total driving population (see Table 3) was estimated using
data from the Bureau of the Census= (U.S.
Population Estimates by Age, Sex, Race and Hispanic Origin: 1990 to 1997), and 1996
Motor Vehicle Occupant Safety Survey. The single-year-of-age population estimates for
November 1997 were aggregated to the categories used in the current study and then
multiplied by the proportion of each age cohort who reported driving in the 1996 study. It
should be kept in mind that this is an estimate of the driving population rather than the
result of an enumeration of the population.

Table 3

Estimate of the Total Population of Drivers

by Age

Age

Total Population*

% Who Drive#

Total Drivers

16-20

19,069,000

81%

15,394,000

21-24

13,785,000

90%

12,426,000

25-34

35,813,000

94%

33,629,000

35-44

44,124,000

96%

42,229,000

45-54

33,948,000

96%

32,558,000

55-64

22,035,000

94%

20,754,000

65+

34,155,000

88%

29,999,000

* U.S. Population Estimates by Age,
Sex, Race and Hispanic Origin: 1990 to 1997, for November 1997, as reported at the
United States Bureau of the Census Web Site at
<http://www.census.gov/population/estimates/nation/natdoc_rl.txt>.

The use of residential listings to identify working residential
exchanges is generally described as "listed-assisted" or "truncated"
RDD sampling. In a series of empirical studies, Brick, et. al. demonstrated that only
about four percent of all telephone households are excluded in national samples using this
method. In addition, these studies indicate that the differences between covered and
uncovered samples are trivial in most instances, although no direct study of the
relationship between driving and having a telephone at home has been made. The principal
advantage of "list assisted" sampling is that an equal probability systematic
sample of telephone numbers can be selected under this procedure and the variances of
estimates from the list-assisted sample are usually lower than those from a clustered
design like the Mitovsky-Waksberg RDD method.

In the third stage sample, a two-digit number was randomly generated by
computer for each Working Residential Hundreds Block selected in the second stage sample.
This third stage sampling process is the random digit dialing (RDD) component. Every
telephone number within the Hundreds Block has an equal probability of selection,
regardless of whether it is listed or unlisted.

The use of RDD sampling eliminates the otherwise serious problem of
unlisted telephone numbers. Nationwide, approximately 20% of all phone subscribers have
unlisted phones. Moreover, significant variation occurs among demographic groups, with the
number of unlisted phones reaching a high of 26% in the West, 29% in large metropolitan
areas, 25% among those earning $5,000-$10,000, and 32% among nonwhites.

The third stage RDD sample of telephone numbers was then dialed by SRBI
interviewers to determine which were currently working residential household phone
numbers. Non-working numbers and non-residential numbers were immediately replaced by
other RDD numbers selected within the same stratum in the same fashion as the initial
number. Ineligible households (e.g., no adult in the household, language barriers) were
also immediately replaced. Non-answering numbers were not replaced until the research
protocol (in this study, a five-call protocol) was exceeded. However, one or more open
numbers per case (e.g., for ever case yet to be completed, there may be one or more
numbers in working categories such as no answer, callback, etc.,) may have been permitted
in order to permit the replicate to be completed within a reasonable period.

SCREENING TO DETERMINE HOUSEHOLD ELIGIBILITY

The sample construction process yielded a population-based,
random-digit dialing sample of telephone numbers. The systematic dialing of those numbers
to obtain a residential contact yielded an unbiased sample of telephone households. The
next step was to select eligible households within the total sample of working numbers.

An adult respondent at each number drawn into the sampling frame was
contacted about the composition of the household. Telephone numbers that yielded
non-residential contacts such as businesses, churches, and college dormitories, were
screened out. Only households, i.e., residences at which any number of related individuals
or no more than five unrelated persons living together, were eligible for inclusion in the
sample. This minimal screening was only to ascertain that the sample of telephone numbers
reached by interviewers are residential households.

SELECTION OF RESPONDENT WITHIN HOUSEHOLD

The multi-stage sampling process described in the previous sections
yielded an unbiased national sample of households with telephones, drawn proportionate to
the population distribution. The final stage required the selection of one respondent per
household for the interview.

A systematic selection procedure was used to select one designated
respondent for each household sampled. The "most recent/next birthday method"
was used for within household selection among multiple eligibles. The Within Household
Selection Procedure is presented in Figure 1. The CATI system alternated the "most
recent" and "next" birthday specification for the selected respondent to
avoid a temporal bias for birthdays before (or after) the field period.

Hello, I'm calling for the U.S. Department of Transportation. We are
conducting a study of Americans' attitudes about driving and current traffic laws. The
interview is completely confidential.

A. First, how many persons age 16 and older live in this household, even
if they are not at home right now? _________ NUMBER OF ADULTS

A1. How many of these persons age 16 and older drive a motor vehicle at
least a few times a year?

_________ NUMBER OF DRIVERS

IF ONLY ONE, ASK TO SPEAK TO THAT PERSON, IF TWO OR MORE, SAY:

B. In order to select just one person to interview, could I speak to the
DRIVER in your household, age 16 and older, who has had the most recent/next birthday?

Respondent is that person . . . . . . . . . . . . . . . . . . . . . 1

Other respondent came to phone . . . . . . . . . . . . . . . . 2

Respondent is not available [arrange callback] . . . . . . . 3

MONITORING OF TELEPHONE INTERVIEWERS

SRBI draws upon a staff of experienced telephone supervisors for
its projects. All supervisors participated in the project training session. In addition,
they underwent an additional review on interview editing instructions, refusal prevention
and conversion, and study issues.

Two types of supervisors are utilized in SRBI telephone surveys: shift
supervisors and monitors. A shift supervisor was on duty each of the 14 weekly shifts.
They were responsible for quality control, maintaining production rates and supervising
the monitors. In addition, SRBI assigned one monitor for every 10 interviewers.

Each interviewer was silently monitored by a line monitor at least
twice each interviewing shift. The study monitor sat at a CRT where he/she can see what
the interviewer has recorded, while audio-monitoring the interview. The audio-monitoring
allowed the supervisor to determine the quality of the interviewer's performance in terms
of:

6) How well questions from the respondent are handled without
alienating the respondent or biasing his/her response;

7) Avoiding bias by either comments or vocal inflection;

8) Ability to persuade wavering, disinterested or hostile respondents
to continue the interview; and

9) General professional conduct throughout the interview.

The supervisor also monitored the interviewer's recording of survey
responses on the CRT monitor. The supervisor's CRT emulates the interviewer's CRT.
Consequently, the supervisor was able to see whether the interviewer entered the correct
code, number or verbatim response to the question.

INITIAL CONTACT

Initial telephone contact was attempted during the hours of the day
and days of the week which have the greatest probability of respondent contact. The
primary interviewing period was from 5:30 p.m. to 10:00 p.m. on weekdays, from 9:00 a.m.
to 10:00 p.m. on Saturdays, and from 10:00 a.m. to 10:00 p.m. on Sundays (all times are
local time). Since interviewing was conducted across time zones, the interviewing shift
lasted until 1:00 a.m. Eastern Time (10:00 p.m. Pacific Time).

If the interview was not conducted at the time of initial contact, the
interview was rescheduled at a time convenient to the respondent. Although initial contact
attempts were made on evenings and weekends, daytime interviews were scheduled when
necessary. If four telephone contacts on the night and weekend shifts did not elicit a
respondent contact, the fifth contact was attempted on a weekday.

Interviewers attempted a minimum of five calls to each telephone
number. When the household was reached, the interviewer asked to speak to an adult to
screen the household for eligibility and to determine the designated respondent. When the
designated respondent was reached but an interview at that time was inconvenient or
inappropriate, interviewers set up appointments with respondents. When contact was made
with the household, but not the designated respondent(s), interviewers probed for
appropriate callback times and attempted to set up an appointment.

SPANISH LANGUAGE INTERVIEWERS

Spanish language versions of the two survey instruments were
developed in order to eliminate language barriers for a small proportion of the U.S. adult
population. If the interviewer encountered a language barrier at the telephone number,
either with the person answering the phone or with the designated respondent, the
interviewer thanked the person and terminated the call. If the case was designated as
Spanish language, it was turned over to the next available Spanish-speaking interviewer.
All households in which a language barrier (Spanish) was encountered were assigned to a
Spanish-speaking interviewer. These bilingual interviewers re-contacted the
Spanish-speaking households to screen for eligibility and conduct interviews with eligible
respondents.

REFUSAL CONVERSION

The process of converting terminations and refusals, once they had
occurred, involved the following steps. First, there was a diagnostic period, when
refusals and terminates were reported on a daily basis and the Project Director and
Operations Manager reviewed them after each shift to see if anything unusual was
occurring. Second, after enough time had passed to see a large enough sample of refusals
and terminations, the Project Director and his staff developed a refusal conversion
script. Third, the refusal conversion effort was fielded with re-interview attempts
scheduled about a week after the initial refusal. Fourth, the Project Director and
Operations Manager received the outcomes of the refusal conversion efforts on a daily
basis. Minor revisions of the script and the procedures were made, as needed. The final
refusal conversion script is shown in Figure 2 (next page).

FIGURE 2

Refusal Conversion Script

Hello, I

=m________
calling for The U.S. Department of Transportation. This is absolutely not a sales call. We
are conducting an important study of American=s attitudes about driving and current driving
laws. The results will be used to evaluate public awareness of issues related to driving.
Once again, I assure you that I have nothing to sell either now or later.

How did you get my number?

We don

=t know your name or address. The telephone number was selected
randomly in your area, and participation by an adult member of your household will go
towards guaranteeing fair representation of households in your region. The interview is
completely confidential. May we begin?

Why do you need to speak to the next/last birthday respondent?

Our goal is to interview as much of a cross section of the
American population as possible in each of the fifty states. When reaching a household
with more than one resident, we use the birthday selection process to guarantee randomness
and representation. Let me ask you the first question....

I don

=t have time?

I understand. Your participation in this important public opinion
poll is very important. Much of the driving issues awareness among Americans is polled in
this manner. May we contact you tomorrow evening when it

=s more
convenient? We=d appreciate the opportunity to present you with our survey
questionnaire when it=s most convenient for you.

What

=s this about?

Several thousand Americans will be interviewed for this highly
topical driving issues opinion poll being conducted for the U.S. Department of
Transportation and the National Highway Traffic Safety Administration. Important and
highly topical safety issues dealing with driving laws, drivers, etc. are contained in our
questionnaire. Your opinions will be combined with those from your region and state.
Therefore, your participation is rather important in our goal of fairly representing views
from your part of the country. Let me ask you the first question....

If the respondent wonders how we got their number, whether unlisted
or not:

Your telephone number was selected randomly by computer to include
your opinions among those in your region. We don't know your name or any other information
about you. Only a certain number of households have been selected to represent the
opinions of people across the nation. May I begin now?"

FIELD OUTCOMES

The field interviewing for the study commenced on February 20,
1997, following training of the field interviewers, and was completed on April 11, 1997.
Status of cases as of the end of the field period are reported using the categories
defined below.

FIGURE 3

Sample Disposition Categories

NIS/DIS/change # The
number was not in service, had been disconnected, or yielded a
recording indicating that it was no longer an active number

Non-residential The number
yielded a contact with a business, government agency,
pay telephone or other non-residential unit

Computer/fax The
number yielded an electronic tone indicating a fax machine or data
line

No answer
The number
rang, but no one answered

Busy
A busy signal was encountered

Answering machine An answering machine was reached at the telephone
number

Language
The
interview could not be completed because of language barriers

Away for duration The designated respondent was out of the area for
the entire field period

Callback
Contact was
made with the household, but not necessarily the
designated respondent. By the end of the field period, the case had
neither yielded a refusal or completed interview

Callback to complete The interview was interrupted, but not terminated.
The field period
ended before the full interview could be completed

Refusal -- Initial Someone in the household refused to participate in the
study

Refusal -- Second During a refusal conversion attempt, a second refusal to
participate in the study was encountered

Terminate A respondent began the interview but refused to finish

Complete An interview was completed with the designated respondent

In total, 21,415 randomly selected telephone numbers were sampled
within a geographically stratified national sampling frame, with the following results:

24% of the numbers were not active residential phone numbers,
including 9% not-in-service, 12% business or government, and 3% computer or fax tones;

15% of the numbers were no answers (despite repeated attempts) and 7%
were answering machines; and

3% were households in which the designated respondent was not
interviewable (away for an extended period, incapacitated, or deaf) and an additional 1%
were non-interviewable due to language barriers (non-Spanish).

At the close of the field period, only 684 cases (3%) were in callback
status.

The participation rate represents one of the most critical measures of
potential sample bias because it indicates the degree of self-selection by potential
respondents into or out of the survey. The participation rate is calculated as the number
of completed interviews (a successful interview) plus respondents who screen out as
ineligible (assumed to be a successful interview if an eligible person wound have been
found) divided by the number of contacts (possibility of a successful interview existed C the sum of completed interviews, terminated
interviews, screen outs and refusals to interview). The inclusion of screen outs in the
numerator and denominator is mathematically equivalent to discounting the refusals by the
estimated rate of non-eligibility among refusals, that is, it assumes that screen outs
will be found in the same proportion among refusals as they were found among non-refusals.
The participation rate is based on the following elements:

6,000 completed interviews;

1,244 cases in which someone in the household completed the household
screen, but no one in the household was found to be eligible for the full interview; and

2,427 refusals to be interviewed (including 1,419 second refusals)
and 190 terminated interviews.

Based on the standard calculations of participation rate, the
participation rate for this survey was 73.5 percent.

The Final Summary Disposition sample is given in Table 4 (next page).
The average interview length for the survey was 30 minutes.

TABLE 4

Sample
Disposition

TOTAL NUMBERS DIALED

21,415

Ineligible Numbers

Not in Service/Disconnected/Changed Number/Wrong Number

Non-residential

Computer/FAX

Other Reason Terminating

5,208

1,916

2,625

568

99

ELIGIBLE NUMBERS

[Total Numbers Dialed -
Ineligible Numbers

]

16,207

Non-Contacts

No Answer

Answering Machine

Busy

Callback

Not Available

5,637

3,233

1,554

163

684

3

Non-Interviews

Language

Health/Deaf/Deceased

Away for Duration

709

13

560

136

TOTAL CONTACTS

[Eligible Numbers
- Non-Contacts - Non-Interviews]

9,861

Non-Participants

Refusals

(Eligibility Unknown)

(Initial)

(Second)

Screen out

3,671

2,427

(163)

(845)

(1,419)

1,244

TOTAL QUALIFIED

[Total Contacts -
Non-Participants]

6190

Callback to complete

Terminates

0

190

COMPLETES

6,000

PARTICIPATION RATE

[(Completes +
Screen out) / (Total Contacts)]

73.46%

SAMPLE WEIGHTING

The characteristics of a perfectly drawn sample of a population
will vary from true population characteristics only within certain limits of sample
variability (i.e., sampling error). Unfortunately, social surveys do not permit perfect
samples. The sampling frames available to survey research are less than perfect. The
absence of perfect cooperation from sampled units means that the completed sample will
differ from the drawn sample. In order to correct these known problems of sample bias, the
achieved sample is weighted to certain characteristics of the total population.

The weighting plan for the survey was a multi-stage sequential process
of weighting the achieved sample to correct for sampling and non-sampling biases in the
final sample. The first stage in the sample weighting procedures was designed to correct
the cases in the completed sample for known selection biases in the sampling procedures.
At the household selection stage, a random digit dialing process will give households with
more than one telephone number an unequal likelihood of selection. Nationally, about 18%
percent of households selected by random digit dialing will have more than one telephone
number. This selection bias was corrected by giving each household a first stage weight
equal to .5 if there was more than one different telephone number in the household.

The second step in the weighting process was to correct for selection
procedures that yielded unequal probability of selection within sampled households.
Although the survey was designed as a population survey, only one eligible person per
household could be interviewed (because multiple interviews per household are burdensome
and introduce additional design effects into the survey estimates). A respondent's
probability for selection is inverse to the size (number of other eligible adults) of the
household. Hence, the second stage weight was equal to the number of eligible respondents
within the household.

The final step in the weighting process was designed to correct for the
fact that the total number of cases in the weighted sample was larger than the unweighted
sample size because of the use of the number of eligibles weight. In order to avoid
misinterpretation of sample size, the total number of cases in the unweighted sample was
divided by the total number of cases in the weighted sample to yield a sample size weight.
When this weight is applied, the size of the weighted sample is identical to the size of
the unweighted sample.

The final weight (WEIGHT3) incorporates all of the intermediate
weighting steps described above. The final weight adjusts the 6,000 completed interviews
in the achieved sample corrects for known sampling and participation biases, while
maintaining the unweighted sample size.

PRECISION OF SAMPLE ESTIMATES

The objective of the sampling procedures used on this study was to
produce an unbiased sample of the target population. An unbiased sample shares the same
properties and characteristics of the total population from which it is drawn, subject to
a certain level of sampling error. This means that with a properly drawn sample we can
make statements about the properties and characteristics of the total population within
certain specified limits of certainty and sampling variability.

The confidence interval for sample estimates of population proportions,
using simple random samplingwithout replacement, is calculated by the following formula:

Where:

var (x) = the expected sampling error of the mean of some

variable, expressed as a proportion

p = some proportion of the sample displaying a certain

characteristic or attribute

q = (1 - p)

z = the standardized normal variable, given a specified

confidence level (1.96 for samples of this size).

n = the size of the sample

Using this formula, we can estimate that the maximum expected sampling
error at the 95% confidence level (i.e., in 95 out of 100 repeated samples) for a total
sample of 6,000 is + 1.3 percentage points. It should be noted that the maximum
sampling error is based upon the conservative estimate that p = q = 0.5.

The sample sizes for the surveys are large enough to permit estimates
for subsamples of particular interest. Table 5 (next page) presents the expected size of
the sampling error for specified sample sizes of 6,000 and less, at different response
distributions on a categorical variable. As the table shows, larger samples produce
smaller expected sampling variances, but there is a constantly declining marginal utility
of variance reduction per sample size increase.

TABLE 5

Expected
Sampling Error (Plus or Minus)

At the 95% Confidence Level

(Simple Random Sample)

Size of Sample or
Subsample

Percentage of the Sample or
Subsample Giving A Certain Response or Displaying a Certain Characteristic

for Percentages Near:

10 or 90

20 or 80

30 or 70

40 or 60

50

6,000

3,000

2,000

1,500

1,300

1,200

1,100

1,000

900

800

700

600

500

400

300

200

150

100

75

50

0.8

1.1

1.3

1.5

1.6

1.7

1.8

1.9

2.0

2.1

2.2

2.4

2.6

2.9

3.4

4.2

4.8

5.9

6.8

8.4

1.1

1.4

1.8

2.0

2.2

2.3

2.4

2.5

2.6

2.8

3.0

3.2

3.5

3.9

4.5

5.6

6.4

7.9

9.1

11.2

1.2

1.6

2.0

2.3

2.5

2.6

2.7

2.8

3.0

3.2

3.4

3.7

4.0

4.5

5.2

6.4

7.4

9.0

10.4

12.8

1.3

1.8

2.1

2.5

2.7

2.8

2.9

3.0

3.2

3.4

3.6

3.9

4.3

4.8

5.6

6.8

7.9

9.7

12.2

13.7

1.3

1.8

2.2

2.5

2.7

2.8

3.0

3.1

3.3

3.5

3.7

4.0

4.4

4.9

5.7

6.9

8.0

9.8

11.4

14.0

NOTE: Entries are expressed
as percentage points (+ or -).

Given extremely small differences in the confidence intervals for this
sample and those expected for a simple random sample, the general formula for estimating
confidence intervals for a simple random sample will normally be a perfectly reasonable
guide for estimating sampling error for this sample. However, in order to conduct a
specific interval for estimates from sample, the appropriate statistical formula for
calculating the allowance for sampling error (at a 95% confidence interval) in a
stratified sample is:

where:

ASE = allowance for sampling error at the 95% confidence level;

h = a sample stratum;

g = number of sample strata;

wh = stratum h as a proportion of total population;

fh = the sampling fraction for group h -- the number in the

sample divided by the number in the universe;

sh2 = the variance in the stratum h -- for
proportions this

is equal to ph (1.0 - ph);

nh = the sample size for the stratum h.

Although Table 5 provides a useful approximation of the magnitude of
expected sampling error, precise calculation of allowances for sampling error requires the
use of this formula.

ESTIMATING STATISTICAL SIGNIFICANCE

The estimates of sampling precision presented in the previous section
yield confidence bands around the sample estimates, within which the true population value
should lie. This type of sampling estimate is appropriate when the goal of the research is
to estimate a population distribution parameter. However, the purpose of some surveys is
to provide a comparison of population parameters estimated from independent samples (e.g.,
annual tracking surveys) or between subsets of the same sample. In such instances, the
question is not simply whether or not there is any difference in the sample statistics
which estimate the population parameter, but rather is the difference between the sample
estimates statistically significant (i.e., beyond the expected limits of sampling error
for both sample estimates).

To test whether or not a difference between two sample proportions is
statistically significant, a rather simple calculation can be made. Call the total
sampling error (symbolized as var (x) in the formula on page 14) of the first sample s1
and the total sampling error of the second sample s2. Then, the sampling error
of the difference between these estimates is sd which is calculated as:

Any difference between observed proportions that exceed sd is a
statistically significant difference at the specified confidence interval. Note that this
technique is mathematically equivalent to generating standardized tests of the difference
between proportions. An illustration of the pooled sampling error between subsamples for
various sizes is presented in Table 6. This table can be used to indicate the size of
difference in proportions between drivers and non-drivers or other subsamples that would
be statistically significant.

Tarnai, J., Rosa, E. and Scott, L. An Empirical Comparison of the Kish
and the Most Recent Birthday Method for Selecting a Random Household Respondent in
Telephone Surveys. Presented at the Annual Meeting of the American Association for Public
Opinion Research, Hershey, PA, 1987.